Determining Target Audiences for Data Visualizations
When designing a data visualization, you first must clearly consider your target audience. Through what medium will the data visualization be conveyed and for what purpose? What’s the skill level of your audience? What response do you want to invoke through the visualization? After you formulate the answers to these questions in your mind, you’re ready to design your visualization with pen and paper, and then build it in digital form.
Visualizations for visual analysis
People often think that data visualizations are useful only for communicating significant insights, but you can use them as a vehicle of discovery, as well. If you create a loosely focused visualization that offers a lot of context, then a viewer can deduce meaningful trends, patterns, and outliers simply by looking at the data plots and charts. Analysts, scientists, and engineers commonly employ this visual method of data analysis.
Whether you’re designing a piece for the boardroom or for a journal publication, if you’re using data visualization to display complex relationships or outcomes to a highly analytical audience, then you should consider adding a lot of contextual data. This contextual data enables audience members to look at the visualization, think for themselves, and draw their own conclusions.
Visualizations for good storytelling
You may design a data visualization that’s meant solely to convey your findings; generally, when you’re designing for a non-technical audience. In some cases, this type of data visualization might be used for a data-journalism piece in a local newspaper, but at other times, you may use data analytic dashboards to convey a data-driven story to organizational decision makers. Either way, when you’re designing a data visualization that tells the story of your findings, make sure to keep the visualization very clear.
You may include a moderate amount of contextual data, but you want to make sure the visualization is narrowly focused to communicate only the findings you want to convey. An overly complex visualization just confuses a non-technical audience and prevents them from extracting value. If you found some significant stories in your data and want to communicate those stories to your audience, make sure to use little context, a lot of focus, and perhaps even some design choices that reinforce the point you’re trying to make.
Visualizations that make a statement
Sometimes data visualizations are designed for the sole purpose of making a point. If you want to design a piece that provokes, outrages, or elates your audience members, then consider using data art. In data art, you use no context whatsoever. This type of visualization is laser-focused to exact an emotional response in your audience.
It’s called art because powerful artistic design is often employed so that the visualization can really hit home with the audience. Data art is useful if you’re trying to elicit support for a cause or political party. Audience members aren’t meant to analyze anything — they’re meant only to look at your data art and subsequently react in a strongly positive or negative way.